Literature DB >> 9538347

Why and how to control for age in occupational epidemiology.

D Consonni1, P A Bertazzi, C Zocchetti.   

Abstract

In occupational epidemiology, the need to consider the age factor properly influences the choice of study design and analytical techniques. In most studies, age is viewed as a potential confounder. Age is strongly associated with end points of interest in occupational epidemiology (diseases, physiological characteristics, doses of xenobiotics, etc), but to measure age as a confounder it must be associated with the exposure under study. When the exposure of interest is time related-for example, duration of employment, time since first exposure, cumulative exposure-a strong intrinsic association with age can be anticipated, and age will behave as a (usually strong) confounder. When occupational exposures without a direct relation with age-for example, job, department, type of exposure-are evaluated, the degree and direction of confounding bias cannot be anticipated. Control of the confounding effect of age can be accomplished in the design phase of a study by way of randomisation, restriction, and matching. Randomisation is seldom viable in occupational settings. Restriction is rarely used in the case of age. Matching is often used in a case-control study as a method to increase the study efficiency, but it must be followed by proper matched or stratified analysis. Options for age adjustment in the analysis phase involve stratification and regression methods. In longitudinal studies the modified life table analysis is used to take into account the fact that subjects cross categories of age as the study proceeds. Stability of relative measures of effect over age strata favoured the greater use of relative risks than risk differences. In the presence of effect modification the influence of age should not be eliminated; its interaction with exposure should be explicitly considered.

Mesh:

Year:  1997        PMID: 9538347      PMCID: PMC1128946          DOI: 10.1136/oem.54.11.772

Source DB:  PubMed          Journal:  Occup Environ Med        ISSN: 1351-0711            Impact factor:   4.402


  14 in total

1.  Mustard gas poisoning, chronic bronchitis, and lung cancer; an investigation into the possibility that poisoning by mustard gas in the 1914-18 war might be a factor in the production of neoplasia.

Authors:  R A CASE; A J LEA
Journal:  Br J Prev Soc Med       Date:  1955-04

2.  Statistical aspects of the analysis of data from retrospective studies of disease.

Authors:  N MANTEL; W HAENSZEL
Journal:  J Natl Cancer Inst       Date:  1959-04       Impact factor: 13.506

3.  Methodological problems of time-related variables in occupational cohort studies.

Authors:  N Pearce
Journal:  Rev Epidemiol Sante Publique       Date:  1992       Impact factor: 1.019

4.  Standardization of risk ratios.

Authors:  O S Miettinen
Journal:  Am J Epidemiol       Date:  1972-12       Impact factor: 4.897

5.  Potential pitfall in using cumulative exposure in exposure-response relationships: demonstration and discussion.

Authors:  M M Finkelstein
Journal:  Am J Ind Med       Date:  1995-07       Impact factor: 2.214

6.  [Efficacy of the use of barrier creams in the prevention of dermatological diseases in textile dyeing and printing plant workers: results of a randomized trial].

Authors:  P G Duca; G Pelfini; G Ferguglia; L Settimi; C Peverelli; I Sevosi; G Terzaghi
Journal:  Med Lav       Date:  1994 May-Jun       Impact factor: 1.275

7.  Cohort mortality and nested case-control study of lung cancer among structural pest control workers in Florida (United States).

Authors:  A C Pesatori; J M Sontag; J H Lubin; D Consonni; A Blair
Journal:  Cancer Causes Control       Date:  1994-07       Impact factor: 2.506

8.  Survey of methods and statistical models used in the analysis of occupational cohort studies.

Authors:  P W Callas; H Pastides; D W Hosmer
Journal:  Occup Environ Med       Date:  1994-10       Impact factor: 4.402

9.  Confounding: essence and detection.

Authors:  O S Miettinen; E F Cook
Journal:  Am J Epidemiol       Date:  1981-10       Impact factor: 4.897

10.  Cancer in a young population in a dioxin-contaminated area.

Authors:  A C Pesatori; D Consonni; A Tironi; C Zocchetti; A Fini; P A Bertazzi
Journal:  Int J Epidemiol       Date:  1993-12       Impact factor: 7.196

View more
  4 in total

1.  Confounding and confounders.

Authors:  R McNamee
Journal:  Occup Environ Med       Date:  2003-03       Impact factor: 4.402

2.  Integrating affective and cognitive correlates of heart rate variability: A structural equation modeling approach.

Authors:  Sarah L Mann; Edward A Selby; Marsha E Bates; Richard J Contrada
Journal:  Int J Psychophysiol       Date:  2015-07-10       Impact factor: 2.997

3.  Sparse boosting for high-dimensional survival data with varying coefficients.

Authors:  Mu Yue; Jialiang Li; Shuangge Ma
Journal:  Stat Med       Date:  2017-11-19       Impact factor: 2.373

4.  Orchestrated increase of dopamine and PARK mRNAs but not miR-133b in dopamine neurons in Parkinson's disease.

Authors:  Falk Schlaudraff; Jan Gründemann; Michael Fauler; Elena Dragicevic; John Hardy; Birgit Liss
Journal:  Neurobiol Aging       Date:  2014-03-22       Impact factor: 4.673

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.